indicates fraud. One of the most important qualities or advantages of this model is that it understands the data trend and generates the result. Once the result is generated, the model checks how close the result is to the actual results. Based on this analysis, the model adjusts its weights to give an accurate result next time. Once this model has been trained to give accurate results, it can also be used to analyze other data. Even when neural networks are widely accepted, they are not really used in the marketing industry simply due to the fact that preparing data for this model is time consuming and very complex compared to regression analysis . Marketers are very comfortable with regression analysis on neural networks because of the ease of interpreting regression analysis results.4.4 Genetic Algorithm ModelsGenetic algorithms provide a process of holistic research based on the principles of natural genetics and survival of the fittest……4.5 Multiple Regression ModelsMultiple regression is a sophisticated modeling technique, this model predicts consumer behavior on the basis of many attributes at the same time in the process, unlike a single attribute in single linear regression. Unlike simple linear regression, this model includes multiple predictors or independent variables that help us achieve the dependent variables. In marketing terms, independent variables can be age, income, product affinity, etc. and the dependent variable is the answer to the marketing question, such as what are the chances that a particular customer segment will respond positively to a marketing promotion. This model is used by direct marketers to create powerful targets...... middle of paper since the reward is tangible. Since 80 percent of profits come from a small percentage of customers, programs should be developed to retain them. Companies will use resources not available to the general customer base to ensure they retain their most valuable customers and offer incentives to encourage others to progress. 4.8 Next Best Product Companies like Amazon and Netflix are very good at predicting what customers normally buy. and look. Knowing what your customers are or aren't buying will allow you to position products they are statistically likely to purchase based on recent transactions and activities. This is a powerful tool for Netflix because it keeps users engaged and actively using the service, but also allows them to shift their content investments toward elements more likely to keep users active on their site..